Data Analytics

Postgraduate Diploma

While the amount of data being produced is proliferating at a staggering rate, the skills to extract information and the value we receive from it are both relatively scarce. If you are looking to start a career in data science, or even further your current career, our Online Data Analytics PgDip will provide you with vital skills required to develop your data handling expertise. You will gain a firm grounding in the principles of learning from data sets, whilst at the same time learning how to handle, visualise and model data to benefit your organisation.

The Statistics Group at the University of Glasgow is internationally renowned for its research excellence. Students are able to benefit from this by learning from academics whose expertise covers a range of topics from biostatistics and statistical genetics to environmental statistics, statistical methodology and modelling.

We will help you to analyse the true capacity of huge datasets, which will help you to realise your true potential while making you in demand in the modern workplace. Statistical analysis and data mining are currently ranked the second most in-demand hard skills (LinkedIn, 2018).

Designed for part time study, this programme allows you to gain a postgraduate diploma degree from a leading university while youre still in full-time employment. Plus, from day one you can start to put your new knowledge to the test at work. You won't have to wait until you have graduated to make a real difference in the workplace.

Our students come from a variety of sectors including finance, the pharmaceutical industry, banking and government statistical services amongst others.

You will have the freedom to work at your own pace and access to a wide range of learning tools including rich interactive reading material, tutor-led videos and computer-led programming sessions.

PROGRAMME OUTCOMES

Demonstrate thorough understanding of the concepts, principles, theories and methods of probability, statistics and machine learning

Identify links between different statistical concepts and methods in depth

Apply statistical methods to analyse and model data from a variety of contexts

Critique different approaches to modelling data in a given context and critically appraise and synthesise the results obtained from different approaches

Design, develop, use and critically evaluate software for handling and analysing data

Present the results of a statistical analysis in clear oral and written reports

CAREER OUTLOOK

There is a massive shortage of data-analytical skills in the workforce. Statistical analysis and data mining is ranked 2nd in the Top 10 Most In-Demand Hard Skills 2018 by LinkedIn.

This programme offers a multitude of career opportunities and boosts to student career trajectories.

Contact Us

Email us at: maths-stats-analyticsmsc@glasgow.ac.uk

Why Glasgow?

Ranked 1st in the Russell Group for teaching - National Student Survey 2018

Ranked in the top 100 of the world's universities - QS World University Rankings 2019

Meet Our Faculty

Dr Charalampos Chanialidis

Programme Director

Charalampos is a Lecturer in the School of Mathematics and Statistics and Director of the Data Analytics programme. His research interests include Bayesian inference, computational statistics, Markov chain Monte Carlo methods, and machine learning.

Courses

This flexible part-time programme is completed over two years. You will take two courses each trimester.

Programme alteration or discontinuation The University of Glasgow endeavours to run all programmes as advertised. In exceptional circumstances, however, the University may withdraw or alter a programme. For more information, please see: Student Contract

Compulsory

STATS5066Data Management and Analytics Using SAS10 Credits

An in-depth introduction to the statistical software package SAS, including the use of Structured Query Language (SQL). This course covers all the features required for SAS certification as a Certified SAS Base Programmer and a Certified SAS Statistical Business Analyst.

STATS5073Advanced Predictive Models 10 Credits

Looking at models which can account for a non-normal distribution of the response and/or the fact that data is not independent, but correlated. You will gain an overview of different generalisations of linear regression models and become acquainted with the theory of exponential families. You’ll also be introduced to generalised linear models and the concept of a time series.

STATS5075Learning from Data10 Credits

This course will introduce you to different approaches to learning from data, with a focus on interval estimation, hypothesis testing and frequentist and Bayesian model-based inference. You will then learn how to implement these statistical methods using R.

STATS5076Predictive Modelling10 Credits

This course will introduce you to predictive modelling using multiple linear regression as a showcase. It will present some of the distributional theory underpinning the normal linear models and the associated methods for testing and interval estimation. You will also find out how the design matrix of a linear model can be constructed to accommodate categorical covariates or, through basis expansions, non-linear effects.

STATS5077Probability and Stochastic Models10 Credits

Provides a structured development of probability theory and its use to construct stochastic models. Your learning will place emphasis on the theory of random variables and random vectors to help solve real-life problems. The pace of the course is brisk, as it begins from the assumption that you have little prior exposure to probability yet reaches advanced concepts by the end.

STATS5078R Programming 10 Credits

Designed to introduce you to programming in the statistical software environment R. You’ll be introduced to basic concepts and ideas of a statistical computing environment and trained in programming tools which use the R computing environment. The course provides computational skills which will support other courses on the programme and you will learn the fundamental concepts in scientific programming.

STATS5079Data Analytics in Business and Industry10 Credits

The course introduces you to applications of data analytics in business and industry and introduces students to the social, ethical, legal, and professional issues arising in data science. It also delivers experience in the communication and presentation of results.

This course will provide you with a grounding in data mining and machine learning methods used in big data scenarios. You will also learn methods for analysing networks and unstructured data, as well as formal methods for social network analysis and quantitative text analysis.

STATS5082Data Programming in Python10 Credits

This course will introduce you to object-oriented programming and Python as a generic programming language and its use for data programming and analytics. You will learn to use Python libraries that are relevant to data analytics such as scikit-learn, NumPy/SciPy and pandas.

STATS5083High Performance Computing for Data Analytics10 Credits

The course focuses on high-performance computing and presents an overview of big data systems. You’ll be introduced to Julia as well as fundamental concepts in high-performance computing with a focus on parallelisation. You’ll also be trained in the efficient implementation of computationally expensive data-analytic methods, and introduced to enterprise-level technology relevant to big data analytics such as Spark, Hadoop or NoSQL databases.

STATS5084Uncertainty Assessment and Bayesian Computation10 Credits

Develops the foundations of modern Bayesian statistics and demonstrates how prior distributions are updated to posterior distributions in simple statistical models. You’ll be introduced to advanced stochastic simulation methods such as Markov-chain Monte Carlo. You’ll also find out how to fit Bayesian models using high-level software for Bayesian hierarchical modelling such as BUGS or STAN.

An introduction to machine learning methods and modern data-mining techniques, with an emphasis on practical issues and applications. You’ll be introduced to different methods for dimension reduction and clustering (unsupervised learning), a range of classification methods beyond those covered in the Predictive Modelling course. You’ll also learn about neural networks, deep learning, kernel methods, support vector machines and Gaussian processes.

Admissions Requirements

To be accepted to this programme, you must have:

A first degree equivalent to a UK upper second class honours degree, normally with a substantial mathematics component (at least equivalent to Level-1 courses in Mathematics and Level-2 courses in Calculus and Linear Algebra at the University of Glasgow)

Graduates who only have the equivalent of A-level Mathematics can also be admitted to the programme. However, such candidates are required to work through self-study material provided and complete a pre-sessional course in Elementary Mathematics (scheduled in the two weeks preceding the start of the teaching period of semester 1)

Previous study of Statistics or Computing Science is not required

If English is not your first language, the University sets a minimum English Language proficiency level. This is an IELTS overall score of 6.5 with no sub-test less than 6. If you do not have an IELTS test certificate, equivalent scores in other recognised qualifications may be accepted

To apply to this programme:

You must apply online. As part of your online application, you need to submit the following:

A copy (or copies) of your official degree certificate(s), if you have already completed your degree

A copy (or copies) of your official academic transcript(s), showing full details of subjects studied and grades/marks obtained

Official English translations of the certificate(s) and transcript(s)

One reference letter on headed paper

Evidence of your English Language ability (if your first language is not English)

Any additional documents required for this programme (see Entry requirements for this programme)

Please check that you meet the entry criteria for this programme before you apply.

You have 42 days to submit your application once you begin the process. You may save and return to your application as many times as you wish to update information, complete sections or upload supporting documents, such as your final transcript or your language test.

Key Dates UK/EU

Application Deadline

Start Date

23 Sep 2019

23 Sep 2019

Tuition & Fees

Home/EU: £10,000*

International: £10,000*

*Total cost, incremental payment schedule available. Fee information is subject to change and is for guidance only.How much does the programme cost?Part time fees 1,667 per 20 creditsCan I get help to fund my studies?You may be eligible for help with the cost of the programme.

What it's like to study online

100% online for complete flexibility

Our part-time online programmes are ideal if you're working full-time or have family commitments.

Connect to campus from anywhere

All you need for our online programmes is a device with internet access.

Gain a global perspective

As an online student, you'll be part of an international community of academics and learners.

Learn from the experts

Our world-class teaching and research staff will help you realise your potential.

Interact with everyone

Community building and collaborative learning is a key focus of our online programmes.

Access a multitude of resources

Study using a range of materials, including recorded lectures, live seminars, videos, interactive quizzes, journal articles and ebooks.

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